1. Field of the Invention
The present invention relates to data transmission systems, and, in particular, to equalizer-based receivers.
2. Description of the Related Art
Code-Division Multiple-Access (CDMA) systems allow many users simultaneously to access a given frequency allocation. User separation at the receiver is possible because each user spreads its respective modulated data waveform over a wide bandwidth using a unique spreading code, prior to transmitting the waveform. Such spreading typically involves, e.g., multiplying the data waveform with a user-unique high-bandwidth pseudo-noise binary sequence. At the receiving end, the receiver re-multiplies the signal with the pseudo-noise binary sequence to remove substantially all of the pseudo-noise signal, so that the remaining portion of the signal is just the original data waveform. Ordinarily, users spread their signals using codes that are orthogonal to each other, i.e., do not interfere with one another. However, a common problem is inter-symbol interference (ISI), i.e., distortion of a received signal typically manifested in the temporal spreading and consequent overlap of individual pulses from nearby users to the degree that a receiver cannot reliably distinguish between changes of state representing individual signal elements. ISI can present a significant problem if the power level of a desired signal is significantly lower than the power level of an interfering user (e.g., due to distance) and, at a certain threshold, can compromise the integrity of the received data.
One technique for handling ISI is the use of equalizer-based receivers, which are a promising technology for high-speed data transmission systems, such as High-Speed Downlink Packet Access (HSDPA), a standard that is part of the Third-Generation Partnership Project (3GPP). Equalizer-based receivers typically use linear-channel equalizers to restore the orthogonality of spreading sequences lost in frequency-selective channels, thereby suppressing ISI, such as might occur in a downlink operating under the Wide-Band CDMA (WCDMA) standard (a 3GPP technology). Equalizer-based receivers also have the advantage of being of relatively low complexity for short to moderate signal-delay spreads.
One such equalizer-based WCDMA receiver is a Linear Minimum-Mean Square-Error (LMMSE) receiver, which typically trains itself to form an optimal filter relative to a detected channel response and tracks changes in the channel through adaptation. An LMMSE receiver is relatively complex in terms of computations it performs. An example of an LMMSE receiver is illustrated in FIG. 1. As shown, receiver 100 includes a chip-pulsed matched filter 101, an optimum LMMSE filter 102, and a demodulator 103. Chip-pulsed matched filter 101 multiplies a signal received from an analog-to-digital (A/D) converter by its transmitted pulse shape to minimize ISI, and provides the multiplied signal to optimum LMMSE filter 102. Optimum LMMSE filter 102 minimizes the output signal-to-noise ratio (SNR) for a given channel response that is a composite of all of the underlying multipath signals in the channel. In the example of FIG. 1, the channel is static and known, and therefore, a set of optimum filter coefficients can be obtained using Minimum-Mean Square-Error (MMSE) criteria (whereas, in alternative embodiments, if the channel is slowly varying or unknown, then an adaptive filter could be used). Optimum LMMSE filter 102 provides a filtered signal to demodulator 103, which demodulates (e.g., descrambles, despreads, and de-rotates) the filtered signal, resulting in a sequence of symbols that are provided as soft outputs, which are additionally processed (not shown) to generate the hard outputs of LMMSE receiver 100. Each of processing blocks 101, 102, and 103 is coupled to a digital-signal processor (DSP) core 104 via a DSP address and data bus 106 for exchanging instructions and data with DSP core 104.
Using DSP core 104, optimum LMMSE filter 102 performs complex matrix computations, such as computing tap weights. Various components of other advanced receivers employing, e.g., rake or Maximum-Likelihood Sequence Estimation (MLSE) equalizers, also perform complex matrix computations, such as computing tap locations. Typical low-power DSPs for handheld devices employing such advanced receivers do not have enough processing capacity to perform such computations in software. Thus, higher-power and more complex DSPs are often used to enable such computations. However, the use of such DSPs results in higher current consumption and production cost, and, due to the complexity of the calculations, it takes a relatively long time for DSPs to perform such computations.